AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems

TL;DR

AutoMCU introduces an LLM-based multi-agent system for rapid, feasible neural network customization on MCUs, significantly reducing development time while ensuring deployability and performance.

Contribution

It presents a novel feasibility-first approach combining LLMs and multi-agent coordination for efficient neural network deployment on resource-constrained MCUs.

Findings

AutoMCU achieves competitive accuracy under strict MCU constraints.

Reduces customization time to 1-2 hours from hundreds of GPU hours.

Demonstrates practical deployment on STM32 microcontrollers.

Abstract

Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and computation constraints. Existing approaches, such as model compression and hardware-aware neural architecture search (HW-NAS), often depend on proxy metrics, incur high search cost, and do not fully bridge the gap between architecture design and verified deployment. This paper presents AutoMCU, a feasibility-first large language model (LLM)-based multi-agent system for automated neural network customization under MCU constraints. Given natural-language task requirements and hardware specifications, AutoMCU iteratively generates structured architecture candidates, filters infeasible designs through vendor toolchain feedback before training, evaluates feasible models under a controlled protocol, and verifies deployability through backend-grounded deployment analysis. AutoMCU includes two key mechanisms: 1) hardware-in-the-loop architecture generation for early elimination of undeployable candidates under RAM and Flash constraints, and 2) state-isolated multi-agent scheduling for stable coordination of proposal, training, evaluation, and deployment stages. Experiments on CIFAR-10 and CIFAR-100 under strict MCU constraints show that AutoMCU achieves competitive accuracy while reducing customization time to about 1--2 hours, compared with hundreds of GPU hours for representative MCU-oriented HW-NAS baselines. Comparisons with ColabNAS and the LLM-based NAS method GENIUS on NAS-Bench-201 further demonstrate the effectiveness and stability of AutoMCU. Real-device deployments on multiple STM32 microcontrollers validate its practical applicability to MCU-scale edge intelligence.